Dinghuai Zhang

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 58

Top Topics

Regularized Kernel

Probabilistic Modeling

Combinatorial Problems

Top Venues

Publications

Peiyu Yu, Dinghuai Zhang, Hengzhi He, Xiaojian Ma, Ruiyao Miao, Yifan Lu, Yasi Zhang, Deqian Kong, Ruiqi Gao, Jianwen Xie, Guang Cheng, Ying Nian Wu
Latent Energy-Based Odyssey: Black-Box Optimization via Expanded Exploration in the Energy-Based Latent Space. CoRR (2024)
Ming-Yang Zhou, Zichao Yan, Elliot Layne, Nikolay Malkin, Dinghuai Zhang, Moksh Jain, Mathieu Blanchette, Yoshua Bengio
PhyloGFN: Phylogenetic inference with generative flow networks. ICLR (2024)
George Ma, Emmanuel Bengio, Yoshua Bengio, Dinghuai Zhang
Baking Symmetry into GFlowNets. CoRR (2024)
Minsu Kim, Taeyoung Yun, Emmanuel Bengio, Dinghuai Zhang, Yoshua Bengio, Sungsoo Ahn, Jinkyoo Park
Local Search GFlowNets. ICLR (2024)
Dinghuai Zhang, Yizhe Zhang, Jiatao Gu, Ruixiang Zhang, Josh M. Susskind, Navdeep Jaitly, Shuangfei Zhai
Improving GFlowNets for Text-to-Image Diffusion Alignment. CoRR (2024)
Jean-Pierre Falet, Hae Beom Lee, Nikolay Malkin, Chen Sun, Dragos Secrieru, Dinghuai Zhang, Guillaume Lajoie, Yoshua Bengio
Delta-AI: Local objectives for amortized inference in sparse graphical models. ICLR (2024)
Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen, Aaron C. Courville, Yoshua Bengio
Distributional GFlowNets with Quantile Flows. Trans. Mach. Learn. Res. 2024 (2024)
Dinghuai Zhang, Ricky T. Q. Chen, Cheng-Hao Liu, Aaron C. Courville, Yoshua Bengio
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization. ICLR (2024)
Yimu Wang, Dinghuai Zhang, Yihan Wu, Heng Huang, Hongyang Zhang
Cooperation or Competition: Avoiding Player Domination for Multi-Target Robustness via Adaptive Budgets. CVPR (2023)
Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-García, Léna Néhale Ezzine, Yoshua Bengio, Nikolay Malkin
A theory of continuous generative flow networks. CoRR (2023)
Minsu Kim, Taeyoung Yun, Emmanuel Bengio, Dinghuai Zhang, Yoshua Bengio, Sungsoo Ahn, Jinkyoo Park
Local Search GFlowNets. CoRR (2023)
Ling Pan, Dinghuai Zhang, Moksh Jain, Longbo Huang, Yoshua Bengio
Stochastic Generative Flow Networks. CoRR (2023)
Dinghuai Zhang, Ricky Tian Qi Chen, Cheng-Hao Liu, Aaron C. Courville, Yoshua Bengio
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization. CoRR (2023)
Ling Pan, Nikolay Malkin, Dinghuai Zhang, Yoshua Bengio
Better Training of GFlowNets with Local Credit and Incomplete Trajectories. ICML (2023)
Zhengqi Gao, Dinghuai Zhang, Luca Daniel, Duane S. Boning
Rare Event Probability Learning by Normalizing Flows. CoRR (2023)
Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron C. Courville, Yoshua Bengio, Ling Pan
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets. NeurIPS (2023)
Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-García, Léna Néhale Ezzine, Yoshua Bengio, Nikolay Malkin
A theory of continuous generative flow networks. ICML (2023)
Dianbo Liu, Moksh Jain, Bonaventure F. P. Dossou, Qianli Shen, Salem Lahlou, Anirudh Goyal, Nikolay Malkin, Chris Chinenye Emezue, Dinghuai Zhang, Nadhir Hassen, Xu Ji, Kenji Kawaguchi, Yoshua Bengio
GFlowOut: Dropout with Generative Flow Networks. ICML (2023)
Minsu Kim, Joohwan Ko, Dinghuai Zhang, Ling Pan, Taeyoung Yun, Woochang Kim, Jinkyoo Park, Yoshua Bengio
Learning to Scale Logits for Temperature-Conditional GFlowNets. CoRR (2023)
Ling Pan, Nikolay Malkin, Dinghuai Zhang, Yoshua Bengio
Better Training of GFlowNets with Local Credit and Incomplete Trajectories. CoRR (2023)
Dinghuai Zhang, Aaron C. Courville, Yoshua Bengio, Qinqing Zheng, Amy Zhang, Ricky T. Q. Chen
Latent State Marginalization as a Low-cost Approach for Improving Exploration. ICLR (2023)
Jiaye Teng, Chuan Wen, Dinghuai Zhang, Yoshua Bengio, Yang Gao, Yang Yuan
Predictive Inference with Feature Conformal Prediction. ICLR (2023)
Yimu Wang, Dinghuai Zhang, Yihan Wu, Heng Huang, Hongyang Zhang
Cooperation or Competition: Avoiding Player Domination for Multi-Target Robustness via Adaptive Budgets. CoRR (2023)
Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen, Aaron C. Courville, Yoshua Bengio
Distributional GFlowNets with Quantile Flows. CoRR (2023)
Zhijian Duan, Wenhan Huang, Dinghuai Zhang, Yali Du, Jun Wang, Yaodong Yang, Xiaotie Deng
Is Nash Equilibrium Approximator Learnable? AAMAS (2023)
Ling Pan, Dinghuai Zhang, Aaron C. Courville, Longbo Huang, Yoshua Bengio
Generative Augmented Flow Networks. ICLR (2023)
Jean-Pierre Falet, Hae Beom Lee, Nikolay Malkin, Chen Sun, Dragos Secrieru, Dinghuai Zhang, Guillaume Lajoie, Yoshua Bengio
Delta-AI: Local objectives for amortized inference in sparse graphical models. CoRR (2023)
Mingyang Zhou, Zichao Yan, Elliot Layne, Nikolay Malkin, Dinghuai Zhang, Moksh Jain, Mathieu Blanchette, Yoshua Bengio
PhyloGFN: Phylogenetic inference with generative flow networks. CoRR (2023)
Ling Pan, Dinghuai Zhang, Moksh Jain, Longbo Huang, Yoshua Bengio
Stochastic Generative Flow Networks. UAI (2023)
Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward J. Hu, Katie Everett, Dinghuai Zhang, Yoshua Bengio
GFlowNets and variational inference. ICLR (2023)
Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron C. Courville, Yoshua Bengio, Ling Pan
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets. CoRR (2023)
Dinghuai Zhang, Ricky T. Q. Chen, Nikolay Malkin, Yoshua Bengio
Unifying Generative Models with GFlowNets. CoRR (2022)
Dinghuai Zhang, Hongyang Zhang, Aaron C. Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala
Building Robust Ensembles via Margin Boosting. CoRR (2022)
Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward J. Hu, Katie Everett, Dinghuai Zhang, Yoshua Bengio
GFlowNets and variational inference. CoRR (2022)
Moksh Jain, Emmanuel Bengio, Alex Hernández-García, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ajit Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio
Biological Sequence Design with GFlowNets. ICML (2022)
Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron C. Courville, Yoshua Bengio
Generative Flow Networks for Discrete Probabilistic Modeling. ICML (2022)
Dinghuai Zhang, Hongyang Zhang, Aaron C. Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala
Building Robust Ensembles via Margin Boosting. ICML (2022)
Dinghuai Zhang, Jie Fu, Yoshua Bengio, Aaron C. Courville
Unifying Likelihood-free Inference with Black-box Optimization and Beyond. ICLR (2022)
Dianbo Liu, Moksh Jain, Bonaventure Dossou, Qianli Shen, Salem Lahlou, Anirudh Goyal, Nikolay Malkin, Chris Emezue, Dinghuai Zhang, Nadhir Hassen, Xu Ji, Kenji Kawaguchi, Yoshua Bengio
GFlowOut: Dropout with Generative Flow Networks. CoRR (2022)
Dinghuai Zhang, Aaron C. Courville, Yoshua Bengio, Qinqing Zheng, Amy Zhang, Ricky T. Q. Chen
Latent State Marginalization as a Low-cost Approach for Improving Exploration. CoRR (2022)
Moksh Jain, Emmanuel Bengio, Alex Hernández-García, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio
Biological Sequence Design with GFlowNets. CoRR (2022)
Jiaye Teng, Chuan Wen, Dinghuai Zhang, Yoshua Bengio, Yang Gao, Yang Yuan
Predictive Inference with Feature Conformal Prediction. CoRR (2022)
Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron C. Courville, Yoshua Bengio
Generative Flow Networks for Discrete Probabilistic Modeling. CoRR (2022)
Ling Pan, Dinghuai Zhang, Aaron C. Courville, Longbo Huang, Yoshua Bengio
Generative Augmented Flow Networks. CoRR (2022)
Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron C. Courville
Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization? ICML (2021)
David Krueger, Ethan Caballero, Jörn-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Rémi Le Priol, Aaron C. Courville
Out-of-Distribution Generalization via Risk Extrapolation (REx). ICML (2021)
Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron C. Courville, Zhanxing Zhu
Neural Approximate Sufficient Statistics for Implicit Models. ICLR (2021)
Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron C. Courville
Can Subnetwork Structure be the Key to Out-of-Distribution Generalization? CoRR (2021)
Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio, Ioannis Mitliagkas, Irina Rish
Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization. NeurIPS (2021)
Dinghuai Zhang, Jie Fu, Yoshua Bengio, Aaron C. Courville
Unifying Likelihood-free Inference with Black-box Sequence Design and Beyond. CoRR (2021)
Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Yoshua Bengio, Ioannis Mitliagkas, Irina Rish
Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization. CoRR (2021)
Baifeng Shi, Dinghuai Zhang, Qi Dai, Zhanxing Zhu, Yadong Mu, Jingdong Wang
Informative Dropout for Robust Representation Learning: A Shape-bias Perspective. CoRR (2020)
Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework. CoRR (2020)
Baifeng Shi, Dinghuai Zhang, Qi Dai, Zhanxing Zhu, Yadong Mu, Jingdong Wang
Informative Dropout for Robust Representation Learning: A Shape-bias Perspective. ICML (2020)
Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework. NeurIPS (2020)
Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron C. Courville, Zhanxing Zhu
Neural Approximate Sufficient Statistics for Implicit Models. CoRR (2020)
Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle. NeurIPS (2019)
Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle. CoRR (2019)